Implementation of "Generating Sequences With Recurrent Neural Networks" https://arxiv.org/abs/1308.0850
This project is a web app (CopyMonkey) which uses machine learning to mimic your handwriting style like a monkey. Have fun generating handwriting in your own style!
The ML model is implemented from "Generating Sequences With Recurrent Neural Networks" by Alex Graves (https://arxiv.org/abs/1308.0850)
git clone https://github.com/swechhachoudhary/Handwriting-synthesis.git
python3 -m venv hand_gen_env
pip install -r requirements.txt
python train.py --n_epochs 120 --model synthesis --batch_size 64 --text_req
python train.py --n_epochs 120 --model prediction --batch_size 64
There are 2 data files that you need to consider:
data.npycontains 6000 sequences of points that correspond to handwritten sentences.
sentences.txtcontains the corresponding text sentences. You can see an example on how to load and plot an example sentence in
example.ipynb. Each handwritten sentence is represented as a 2D array with T rows and 3 columns. T is the number of timesteps. The first column represents whether to interrupt the current stroke (i.e. when the pen is lifted off the paper). The second and third columns represent the relative coordinates of the new point with respect to the last point. Please have a look at the plot_stroke if you want to understand how to plot this sequence.
Generated samples: Samples generated using priming: * Prime style text is "medical assistance", text after this is generated by model * Prime style text is "something which he is passing on", text after this is generated by model * Prime style text is "In Africa Jones hotels spring", text after this is generated by model